Computer-aided extraction of semantics from keywords to confirm match of buyer offers to seller bids

ABSTRACT

One embodiment involves a method and apparatus for mapping lexical keywords into entity description semantics in order to create unambiguous buyer-confirmed descriptions of entities. The method described herein relies on a computer program and some mechanism for computer data storage.

RELATED APPLICATION(S)

This is a Continuation Application of U.S. patent application Ser. No. 11/213,145, which was filed on Aug. 25, 2005, which in turn claims priority from U.S. Provisional Application 60/606,357, filed Aug. 31, 2004, which is incorporated herein by reference.

BACKGROUND OF INVENTION

There are many descriptions of computer-aided searches of large search spaces, such as the world wide web, whereby narrowing the search space to a successively smaller and more precise area of interest is accomplished using one or more algorithms involving lexicons.

One problem with the use of lexicons is the limitation inherent in a pure textual search. For example, although a lexical search of the world wide web for matches to “blue sweater” might be refined through human-computer interactions to the more specific “blue sweater crew neck men's large”;, the resulting search result set is likely to include citations for:

(A) Descriptions of an article of men's apparel known as a sweater and having elements of fashion known as a crew-neck and available in size large and extra-large. (B) Descriptions of an article of men's apparel known as a sweater and having elements of fashion known as a crew-neck and available at large department stores. (C) Many reprints and quotes from an often quoted article on the hardworking men on the crew of the Blue Man-Group and their experiences during their tour of large cities.

In the above case, the intended search scope is best characterized by the citation in item A. Item B is closer, however there was no semantic meaning to the keyword “large” to indicate that “large” should be used to modify the size of the article of apparel rather than to modify the size of the department store. Item C is wildly out of scope as compared to the buyer's intended search space, yet scores a bit (match) on the refined search terms.

Even more sophisticated computer-aided lexical searches employing lexical associations do not appreciably and consistently reduce the occurrences of search results returning citations that are wildly outside of the target scope (false hits). One commonly employed partial solution to the shortcomings of a pure lexical search is to inject lexical associations into the lexical refinements. Prior attempts to inject lexical associations into computer-aided searches have relied on the existence of a virtual expert advisor, or other access to a domain-specific knowledgebase. In practice such implementations merely inject lexical associations iteratively, resulting in the construction of longer and longer search strings. This technique car.) result in a rapid narrowing of the search space, however this technique does not reliably eliminate or reduce the occurrence of false hits or wildly out of scope citations.

It has been observed that when humans interact with computer-aided search engines (e.g., Google, eBay.com) in search of products, services or information, they frequently provide keywords that tend to be values or characteristics of the desired products, services or information. For example, when searching for an automobile, the keyword string might be:

-   -   “1997 Mustang red convertible”         where each of the above keywords is the value of an implied         attribute. A human would imply the following attributes;     -   Implied Attributes={Model_year, Model_name, Exterior_color,         Body_style}

Furthermore, a human would infer a mapping of the keywords to attributes as follows:

-   -   Mapping: {Model_year=1997,         -   Model_name=Mustang,         -   Exterior_color=red,         -   Body_style=convertible}

In use, a mapping between the human-specified values/characteristics and the correct corresponding attribute is required in order to enable an unambiguous and effective (i.e., few or no false hits) computer-aided search of a large structured data search space.

Thus, what is desired is a method and apparatus to confirm the mapping between the human-specified values/characteristics and the correct correspondence to characteristics found in an entity description (e.g. product, service, or information), among other techniques to overcome the above, prior art problems (as well as other prior art problems not mentioned).

SUMMARY

One embodiment involves a method and apparatus for mapping lexical keywords into entity description semantics in order to create unambiguous buyer-confirmed descriptions of entities. The. method described herein relies on a computer program and some mechanism for computer data storage.

BRIEF DESCRIPTION OF DRAWINGS

FIG. 1 depicts the process flow and data storage as is commonly used in text searches.

FIG. 2 depicts the data storage elements, processes and examples of semantic category extraction and category confirmation.

FIG. 3 depicts the data storage elements, process and examples of semantic attribute extraction and category attribute confirmation.

FIG. 4 depicts the data storage elements, process and examples of semantic attribute value extraction and category attribute value confirmation.

FIG. 5 depicts an example of a computer-parsable description of hierarchical category description.

FIG. 6 depicts an example of a computer-parsable representation of an item description.

FIG. 7 depicts an example of a computer-parsable representation of the ranking of importance of an item attribute.

FIG. 8 depicts the key protocol exchanges between Server and Client

FIG. 9 depicts examples of

-   -   a computer-parsable representation of Category Attribute DTD     -   a compressed format of the ITEMS ARRAY, and     -   an expansion of the ITEMS ARRAY into a textual/human-readable         format.

DETAILED DESCRIPTION

One method defined in the present embodiment may include the following inputs:

-   -   A list of one of more text strings (110) as have been provided         by the buyer as applicable to the search underway. This list may         contain one or more words specifically excluded as a result of         the lexical keyword refinement.     -   A database (210) containing the union of ail known entity         category descriptions, which may

occur as an enumerated list of hierarchical category descriptions, or in a formal language that permits computer-aided enumeration. An example using a human-readable formal language to describe the levels of the hierarchy is provided in FIG. 5.

-   -   A database (240) containing buyer profiles.     -   A database (350) containing entity descriptions. An example of         an entity description is given in FIG. 6.     -   A database (355) containing a relative scoring of entity         attributes. Such scoring is based on the

empirical or human-entered likelihood of a particular attribute being important to a buyer in search of that entity. An example of this scoring database is given in FIG. 7.

With these inputs then, the mapping from buyer-provided keywords to buyer-confirmed product descriptions occurs through the client-server protocol exchanges of FIG. 8, and more specifically through application of the following operations:

Operation #1;

Given a list of one or more text strings, possibly including buyer-excluded strings, the process (220) scores the quality of the mapping of the text strings against each of the hierarchical category description expansions. For example, given the strings “sweater large crew NOT(children)”, the process (220) may return a high score (i.e., matching to hierarchical levels) for the following hierarchical category descriptions:

-   -   (A) apparel.men.outerwear.sweater (high score because two of the         given strings match)     -   (B) apparel.women.outerwear.sweater (lower score because only         one of the given strings match)

There is also a set of hierarchical category descriptions (e.g., apparel.children.boy.outerwear.sweater) that may receive a low scoring of quality of match due to occurrence of one or more excluded words found in the hierarchical category description.

Finally there is the set of hierarchical category descriptions that receive a zero score due to no matches

of the strings found in the hierarchical category descriptions.

In one embodiment of this operation, words/strings that axe known to be attribute values (e.g., names of colors) and are not likely to be found in any hierarchical category description, are pre-screened from die scoring process, thus reducing compute time required for this operation.

In another embodiment of this operation, one of many scoring algorithms is selected on the basis of the profile (e.g. record in database 240) of a returning buyer, specifically the process 220 increases the score of a particular category description if that category description had been confirmed by the buyer in a previous search. For example, a returning customer with the keyword string “box seat tickets” who had on previous occasions purchased tickets for theatre performances would more likely see theatre-related ticket categories at the top of the list rather than say tickets for Hockey games.

Operation #2:

The highest scoring hierarchical category descriptions are presented to the buyer in order of highest

score toward lowest score. The buyer is then given the opportunity to select the best match of the hierarchical category description as compared to the entity the buyer seeks. In the example:

-   -   apparel.men.outerwear.sweater     -   apparel.men.sportswear.sweater     -   apparel.men.holiday.sweater     -   apparel.women.outerwear.sweater     -   apparel.women.sportswear.sweater     -   apparel.women.holiday.sweater     -   apparel.children.outerwear.sweater     -   apparel.children.sportswear.sweater     -   apparel.children.holiday.sweater     -   entertainment.live-events.tickets.blue-man-group

In one embodiment of this operation, the number of entities in the entity database (350) that correspond to a particular category is displayed to the buyer, thus providing a technique of positive feedback during the search refinement. An example of this is shown in FIG. 2.

In another embodiment of this operation a logically contiguous set of matching categories (known as a category group) are selectively collapsed so as to reduce the number of enumerated matching categories to a smaller set and thus facilitate display to the buyer. In our example, “blue sweater” would match the following ten (10) fully enumerated categories:

-   -   apparel.men.outerwear.sweater     -   apparel.men.sportswear.sweater     -   apparel.men.holiday.sweater     -   apparel.women.outerwear.sweater     -   apparel.women.sportswear.sweater     -   apparel.women.holiday.sweater     -   apparel.children.outerwear.sweater     -   apparel.children.sportswear.sweater     -   apparel.children.holiday.sweater     -   entertainment.live-events.tickets.blue-man-group

In order to display matched categories in a limited display space, we may wish to collapse the categories. Observe that the first nine (9) fully enumerated categories belong to a category group. The collapsed set may thus be reduced to four (4) categories:

-   -   apparel.men . . . sweater<collapsed from outerwear, sportswear         and holiday>     -   apparel.women . . . sweater<collapsed from outerwear, sportswear         and holiday>     -   apparel.children . . . sweater<collapsed from outerwear,         sportswear and holiday>     -   entertainment.live-events.tickets.blue-man-group

In another embodiment of this operation, if a category group was collapsed in order to reduce the number of matching categories, and the buyer selects from among one or more collapsed category groups, the next level(s) of expansion are presented to the buyer for confirmation. For example, if the buyer confirmed the collapsed category “apparel.men . . . sweater”, the corresponding fully enumerated categories are presented for confirmation. Thus, following our example:

-   -   apparel.men.outerwear.sweater     -   apparel.men.sportswear.sweater     -   apparel.men.holiday.sweater

For the purpose of continuing with our example though the following operations, let us assume the buyer selects the category:

-   -   apparel.men.outerwear.sweater.

The buyer-selected category is stored in the storage (230) for use in subsequent operations.

Operation #3:

At the conclusion of Operation #2, the category description to the lowest level of hierarchy is known (that is, the category is known down to that level where no further hierarchical levels are defined). With this confirmed knowledge that the buyer intends to pursue, a search for entities in the specified category, the process (360) makes suggestions to the buyer of most important search parameters (i.e., attributes). The order of presentation to the buyer of the attributes is determined on the basis of:

-   -   The frequency of occurrence of a particular entity attribute as         found in the entity descriptions database (350), and/or     -   The value of the Attribute Quotient Database entry for a         particular entity attribute (355)

The process (360) may scan entity entries found in the Entity Database (350) that are entities corresponding to the category selected in Operation #2. The process (360) may assemble and rank attributes found in those entries and suggest to the buyer a set of attributes most frequently found. In the example, since the buyer selected apparel.men.outerwear.sweater (during Operation #2), the set of attributes displayed to the buyer would include:

-   -   {generic_color, size, generic_style, manufacturer_name,         -   manufacturer_product_name}.

In another embodiment, the Attribute Quotient Database entry for a particular entity attribute is stored as

an array whereby the index of the array corresponds to a particular class of buyer (e.g., an X-generation consumer, a Y-generation consumer, a business-to-business buyer) and each value in the array (i.e., the Attribute Quotient) is determined by behaviors or characteristics of the class of buyer. For example, Y-generation consumers statistically choose The Gap over other designers, while X-generation consumers statistically prefer Ralph Lauren over other designers. The Attribute Quotient Database thus influences the ordering of Category Attributes and the Values of those Category Attributes for presentation to the Buyer in Operation #4.

In one embodiment, the Server (810) of FIG. 8 sends to the Client (820)

-   -   a) structured text in a form similar to the Category Attribute         DTD (910) and     -   b) a computer representation of the Items Array (920).

Thus the Client is able to display the Items Array in a human-readable format (930) using a graphical user interface as further described in Operation #4.

Operation #4:

The buyer is then provided a technique to rank the buyer's relative importance/ranking of the one or more attributes presented for this particular search.

In another embodiment of this Operation #4, one or more graphical user interface devices are presented to the buyer in order to aid buyer ranking of the relative importance of the attributes.

In another embodiment of this Operation the buyer may be offered a choice to select from one or more predefined search parameters that uniquely identify a product (e.g., SKU, or SKU plus color code). Alternatively, in one embodiment, the process 360 may suggest one or more matching Featured Item, and offer the buyer the chance to purchase the corresponding Featured Item.

In another embodiment, the attributes are displayed dynamically, where each successive click results in a new dynamically generated screen that shows the buyer-selected ranking of the attributes. Multiple iterations of buyer clicks followed by repainting of the screen result in a final dynamically generated screen showing all. of the buyer-selected attributes in order of importance to the buyer.

At this point in Operation #4, the buyer has either confirmed buyer's desire to search for matching entities based on an exact match to a SKU (e.g. the buyer selected a Featured Item), or the buyer has

confirmed the relative importance of specific attributes in the parametric search.

In the Operation #4 process 420 the buyer is aided to identify prioritized choices (1^(st) choice, ^(nd) choice, etc.) of values or ranges of values desired (e.g., the availability of a sweater in ‘generic_color=blue’ is acceptable as a first choice and availability in ‘generic_color=green’ is acceptable as a second choice).

In one embodiment, one or more graphical, user interface device(s) are presented to the buyer in order to aid buyer's selection of one or more values or ranges of values of attributes.

In another embodiment, the attribute values are displayed dynamically, where each successive click results in a new dynamically generated screen that shows the buyer-selected ranking of the attribute values. Multiple iterations of buyer clicks followed by repainting of the screen results finally in a dynamically generated screen showing all of the buyer-selected attribute values in order of importance to the buyer.

Operation #5:

At the conclusion of Operation #4, the buyer has either confirmed time precedence of attributes and has indicated preferred values on the basis of actual items available and/or indicated acceptable ranges of values of attributes. An organized array of matching items are presented to the buyer for purchase. In the event the buyer elects not to conclude a transaction, the buyer is given the opportunity to ‘save’ the confirmed criteria (possibly including acceptable ranges for certain attribute values) for subsequent searches. The buyer is presented with options for computer-aided actions to be taken on behalf of the buyer in subsequent searches.

In one embodiment, the buyer is given the opportunity to select one from a group of rule sets, each set containing the rules of exchange including currency designation, exchange rate authority and limits, and a description of acceptable payment instruments (e.g., credit card, P.O., PayPal, etc),

In another embodiment, the buyer's search requirements can be stored in a computer memory and acted on or accessed at a later time. Future action by computer acting as agent for the buyer may inform the buyer of a (newly identified) match or the computer acting as agent for the buyer may perform the transaction on behalf of the buyer.

Following are various optional features that may be optionally included in various embodiments:

-   -   The method of FIG. 1 whereby the buyer's keywords are filtered         to eliminate low value words such as articles (‘a’, ‘the’,         ‘an’), connectives and prepositions.     -   The method of FIG. 1 whereby in case of the presence of         unambiguous keywords, those keywords are mapped to synonyms         known to be in more common use (e.g., the words “clothing” gets         mapped to the synonym “apparel”.     -   The method of FIG. 1 whereby a match of the buyer's keywords to         category descriptions receive a higher score on the basis of         previous full or partial matches as may be recorded in the         buyer's profile record.     -   Category Descriptions of FIG. 2 whereby the hierarchy of the         category descriptions are described in XML.     -   Category Descriptions of FIG. 2 whereby the hierarchy of the         category descriptions are organized as a map of a department         store.     -   Category Descriptions of FIG. 2 whereby the basic item         description is in the form of the AART Product XML dictionary.     -   A technique whereby the buyer is presented with a graphical user         interface page containing three (3) independent         frames/areas: (a) the text search area, (b) the         refinement/feedback area, and (c) the browsing/results area.     -   The method of FIG. 2 whereby the hierarchical/category         description Is confirmed by the buyer using a graphical user         interface.     -   The method of FIG. 3 whereby the buyer can exit the parametric         search at any time and go directly to browsing mode (as         described in item ‘c’ above).     -   The method of FIG. 3 whereby item attributes (and a selection of         values) are proposed to the buyer on the basis of the frequency         of occurrence of an attribute being found among the union of         items In that confirmed category description.     -   The method of FIG. 4 whereby the Entity Description database         (350) contains the rules of exchange including currency         designation, exchange rate authority and limits, and a         description of acceptable payment instruments (e.g., credit         card, P.O., PayPal, etc).     -   Search criteria/results derive guidance for next operation(s) in         the search process whereby at any point during execution of         processes 210, 310, 360, or 420 a list, of products known to         have similar attributes to buyer's criteria are presented to         buyer in a browsable window or frame.     -   The method of FIG. 4 whereby the value or range of values of an         attribute are confirmed by the buyer through a graphical user         interface.     -   The automatic generation of database 355 whereby the database is         generated over time, using actual search results and by capture         and analysis of actual buyers' behaviors.

Those skilled in the art may now recognize that the search space has been reduced from the broad class of retrievable entities that may match one or more text-only keywords, down to a search for one or more entities that belong to a known, unambiguous and specific, category, and further, that a match between the buyer's search criteria and entities can be made on the basis of a scoring system, whereby an exact match is not a necessary condition required before presenting the matching entities to the buyer. 

1. A semantic extraction system comprising: a database of entities available, the database entry of each entity having associated with it one or more entity attribute text strings, and each entity having associated with it at least one category string; first means for capturing user input or selection of one or more desired entity text strings; first scoring and retrieval means for scoring category strings found in the database against the desired entity text strings; first display means for displaying to the user the category strings of highest-scored entities returned by the first scoring and retrieval means; second capture means for capturing user input of user's best match category strings; second display means for displaying the entity attribute strings of the entities corresponding to the said best match category strings; third capture means for capturing user's ranked attribute text strings; second scoring and retrieval means for scoring entity attribute text, strings found in the database against the user's ranked attribute text strings; third display means for displaying to the user the entities returned by the second scoring and retrieval means; fourth capture means for capturing the user's selection of one or mote entities displayed.
 2. The system of claim 1 wherein the user's desired entity text strings are examined and scored against likelihood of a particular keyword being a category descriptor.
 3. The system of claim 1 wherein the user's desired entity text strings are examined and scored against the likelihood of a particular keyword being an attribute value.
 4. The system of claim 1 wherein the category descriptions are proposed to the buyer on the basis of a match found between the buyer's search word and one or more item attribute name(s), AND on the basis of a match found between the buyer's search word and one or more item attribute value(s).
 5. A method, comprising: identifying at least one desired entity text siring; scoring category strings based on the at least one desired entity text string; displaying the category strings based on the scoring of the category strings; identifying at least one best match category string; displaying at least one entity attribute string of entities corresponding to the at least one best match category string; identifying at least one ranked attribute text string; scoring at least one entity attribute text string based on the at least one ranked attribute text string; displaying a plurality of entities based on the scoring of the at least one entity attribute text string; and receiving a selection of at least one of the entities displayed.
 6. A computer program product embodied on a computer readable medium, comprising: computer code for identifying at least one desired entity text string; computer code for scoring category strings based on the at least one desired entity text string; computer code for displaying the category strings based on the scoring of the category strings; computer code for identifying at least one best match category string; computer code for displaying at least one entity attribute string of entities corresponding to the at. least one best match category string; computer code for identifying at least one ranked attribute text string; computer code for scoring at least one entity attribute text string based on the at least one ranked attribute text string; computer code for displaying a plurality of entities based on the scoring of the at least one entity attribute text string; and computer code for receiving a selection of at least one of the entities displayed. 